Association of genotype with CT markers of cerebral amyloid angiopathy in spontaneous intracerebral haemorrhage.

Background And Objective: We investigated the association of alleles with CT-based cerebral amyloid angiopathy (CAA) markers including subarachnoid extension (SAE) and finger-like projection (FLP).

Methods: We included patients with acute primary supratentorial intracerebral haemorrhage (ICH) from a multicentre cohort in China. First, the association of with ICH location (lobar vs non-lobar) was evaluated.

Contrastive voxel clustering for multiscale modeling of brain network.

Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI).

Essential Nutrients and White Matter Hyperintensities: A Two-Sample Mendelian Randomization Study.

Stroke and dementia have been linked to the appearance of white matter hyperintensities (WMHs). Meanwhile, diffusion tensor imaging (DTI) might capture the microstructural change in white matter early. Specific dietary interventions may help to reduce the risk of WMHs.

The value of color doppler ultrasonography combined with computed tomography angiography and magnetic resonance angiography in the preoperative quantification and classification of carotid body tumors: a retrospective analysis.

Background: Computed tomography angiography (CTA) and magnetic resonance angiography (MRA) provide accurate vascular imaging information, but their use may be contraindicated. Color Doppler ultrasonography (CDU) provides simple, safe, noninvasive, and reproducible imaging. We therefore investigated the role of preoperative CDU combined with CTA and MRA in the quantification, typing, and diagnosis of carotid body tumors (CBTs).

Characteristics and Potential Neural Substrates of Encoding and Retrieval During Memory Binding in Amnestic Mild Cognitive Impairment.

Background: Whether encoding or retrieval failure contributes to memory binding deficit in amnestic mild cognitive impairment (aMCI) has not been elucidated. Also, the potential brain structural substrates of memory binding remained undiscovered.

Objective: To investigate the characteristics and brain atrophy pattern of encoding and retrieval performance during memory binding in aMCI.

An extensible hierarchical graph convolutional network for early Alzheimer's disease identification.

Background And Objective: For early identification of Alzheimer's disease (AD) based on multi-modal magnetic resonance imaging (MRI) data, it is important to make comprehensive use of image features and non-image information to analyze the gray matter atrophy and the structural/functional connectivity abnormalities for different courses of AD.

Methods: In this study, we propose an extensible hierarchical graph convolutional network (EH-GCN) for early AD identification. Based on the extracted image features from multi-modal MRI data using the presented multi-branch residual network (ResNet), the brain regions-of-interests (ROIs) based GCN is built to extract structural and functional connectivity features between different ROIs of the brain.

Role of hippocampal subfields in neurodegenerative disease progression analyzed with a multi-scale attention-based network.

Background And Objective: Both Alzheimer's disease (AD) and Parkinson's disease (PD) are progressive neurodegenerative diseases. Early identification is very important for the prevention and intervention of their progress. Hippocampus plays a crucial role in cognition, in which there are correlations between atrophy of Hippocampal subfields and cognitive impairment in neurodegenerative diseases.

CarveMix: A simple data augmentation method for brain lesion segmentation.

Brain lesion segmentation provides a valuable tool for clinical diagnosis and research, and convolutional neural networks (CNNs) have achieved unprecedented success in the segmentation task. Data augmentation is a widely used strategy to improve the training of CNNs. In particular, data augmentation approaches that mix pairs of annotated training images have been developed.

Deep learning-assisted classification of calcaneofibular ligament injuries in the ankle joint.

Background: The classification of calcaneofibular ligament (CFL) injuries on magnetic resonance imaging (MRI) is time-consuming and subject to substantial interreader variability. This study explores the feasibility of classifying CFL injuries using deep learning methods by comparing them with the classifications of musculoskeletal (MSK) radiologists and further examines image cropping screening and calibration methods.

Methods: The imaging data of 1,074 patients who underwent ankle arthroscopy and MRI examinations in our hospital were retrospectively analyzed.

A Multi-scale Attention-based Convolutional Network for Identification of Alzheimer's Disease based on Hippocampal Subfields.

Hippocampus is an important anatomical region for Alzheimer's Disease (AD) identification. In this paper, a multi-scale attention-based convolutional network is proposed for AD identification. The two dimensional (2D) images in three different planes of hippocampal subfields are used as input of three branches of the proposed network, which achieves effective extraction of three dimensional (3D) data features while reducing the network complexity and improving the computational efficiency.

CT-Visible Convexity Subarachnoid Hemorrhage Predicts Early Recurrence of Lobar Hemorrhage.

Background And Purpose: Convexity subarachnoid hemorrhage (cSAH) may predict an increased recurrence risk in cerebral amyloid angiopathy (CAA)-related intracerebral hemorrhage (ICH) survivors. We aimed to investigate whether cSAH detected on CT was related to early recurrence in patients with ICH related to CAA.

Methods: We analyzed data from consecutive lobar ICH patients diagnosed as probable or possible CAA according to the Boston criteria using the method of cohort study.

Multi-resolution 3D-HOG feature learning method for Alzheimer's Disease diagnosis.

Background And Objective: Alzheimer's Disease (AD) is a progressive irreversible neurodegeneration disease and thus timely identification is critical to delay its progression.

Methods: In this work, we focus on the traditional branch to design discriminative feature extraction and selection strategies to achieve explainable AD identification. Specifically, a spatial pyramid based three-dimensional histogram of oriented gradient (3D-HOG) feature learning method is proposed.

Knowledge transfer between brain lesion segmentation tasks with increased model capacity.

Convolutional neural networks (CNNs) have become an increasingly popular tool for brain lesion segmentation in recent years due to its accuracy and efficiency. However, CNN-based brain lesion segmentation generally requires a large amount of annotated training data, which can be costly for medical imaging. In many scenarios, only a few annotations of brain lesions are available.

Super-Resolved q-Space deep learning with uncertainty quantification.

Diffusion magnetic resonance imaging (dMRI) provides a noninvasive method for measuring brain tissue microstructure. q-Space deep learning(q-DL) methods have been developed to accurately estimate tissue microstructure from dMRI scans acquired with a reduced number of diffusion gradients. In these methods, deep networks are trained to learn the mapping directly from diffusion signals to tissue microstructure.

An improved deep network for tissue microstructure estimation with uncertainty quantification.

Deep learning based methods have improved the estimation of tissue microstructure from diffusion magnetic resonance imaging (dMRI) scans acquired with a reduced number of diffusion gradients. These methods learn the mapping from diffusion signals in a voxel or patch to tissue microstructure measures. In particular, it is beneficial to exploit the sparsity of diffusion signals jointly in the spatial and angular domains, and the deep network can be designed by unfolding iterative processes that adaptively incorporate historical information for sparse reconstruction.

Comparison and Analyzation of Different Feature Parameters for Alzheimer's disease Identification.

In this paper, we compare the performance of the derived anatomical features and the extracted feature parameters in Alzheimer's disease (AD) identification. The correlation relationship between them and clinical mini-mental state examination (MMSE) score is analyzed. Based on these feature parameters, the highly correlated combined feature vectors are built and used as variables for the presented modified elastic net (EN) classifier.

Region-of-Interest based sparse feature learning method for Alzheimer's disease identification.

Background And Objective: In recent years, some clinical parameters, such as the volume of gray matter (GM) and cortical thickness, have been used as anatomical features to identify Alzheimer's disease (AD) from Healthy Controls (HC) in some feature-based machine learning methods. However, fewer image-based feature parameters have been proposed, which are equivalent to these clinical parameters, to describe the atrophy of regions-of-interest (ROIs) of the brain. In this study, we aim to extract effective image-based feature parameters to improve the diagnostic performance of AD with magnetic resonance imaging (MRI) data.

Computerized multi-domain cognitive training reduces brain atrophy in patients with amnestic mild cognitive impairment.

The present study aimed to explore the effect of computerized multi-domain cognitive training (MDCT) on brain gray matter volume and neuropsychological performance in patients with amnestic mild cognitive impairment (amnestic MCI). Twenty-one patients with amnestic MCI participated in a computerized MDCT program. The program targeted a broad set of cognitive domains via programs focused on reasoning, memory, visuospatial, language, calculation, and attention.

A Volumetric and Functional Connectivity MRI Study of Brain Arginine-Vasopressin Pathways in Autistic Children.

Dysfunction of brain-derived arginine-vasopressin (AVP) systems may be involved in the etiology of autism spectrum disorder (ASD). Certain regions such as the hypothalamus, amygdala, and hippocampus are known to contain either AVP neurons or terminals and may play an important role in regulating complex social behaviors. The present study was designed to investigate the concomitant changes in autistic behaviors, circulating AVP levels, and the structure and functional connectivity (FC) of specific brain regions in autistic children compared with typically developing children (TDC) aged from 3 to 5 years.

Different post label delay cerebral blood flow measurements in patients with Alzheimer's disease using 3D arterial spin labeling.

Purpose: To evaluate cerebral blood flow (CBF) in patients with Alzheimer's disease (AD) using a three-dimensional pseudocontinuous arterial spin labeling (PCASL). We aimed to study the effects of different post label delay on the resulting CBF maps and to investigate the characteristics and clinical applications of brain perfusion.

Materials And Methods: Sixteen AD patients and nineteen healthy control subjects were recruited.

Quantitative assessment and correlation analysis of cerebral microbleed distribution and leukoaraiosis in stroke outpatients.

Objective: Cerebral microbleeds (CMBs) are bleeding events associated with cerebral small vessel disease (SVD). Strictly lobar CMBs and strictly deep CMBs are likely caused by cerebral amyloid angiopathy (CAA) and hypertensive arteriopathy, respectively. Leukoaraiosis (LA) reflects an ischaemic change in SVD, and LA severity has been correlated with CMBs.

Contrast-enhanced MR angiography: does a higher relaxivity MR contrast agent permit a reduction of the dose administered for routine vascular imaging applications?

Purpose: The authors prospectively compared single dose (0.1 mmol/kg bodyweight) gadobenate dimeglumine with double dose (0.2 mmol/kg bodyweight) gadopentetate dimeglumine for contrast-enhanced magnetic resonance angiography (CE-MRA) in patients with suspected or known steno-occlusive disease of the carotid, renal or peripheral vasculature using an intra-individual crossover study design.

Prognostic value of coronary artery calcium score in patients with stable angina pectoris after percutaneous coronary intervention.

Objectives: To evaluate the prognostic value of the coronary artery calcium (CAC) score in patients with stable angina pectoris (SAP) who underwent percutaneous coronary intervention (PCI).

Methods: A total of 334 consecutive patients with SAP who underwent first PCI following multi-slice computer tomography (MSCT) were enrolled from our institution between January 2007 and June 2012. The CAC score was calculated according to the standard Agatston calcium scoring algorithm.

One-step articular cartilage repair: combination of in situ bone marrow stem cells with cell-free poly(L-lactic-co-glycolic acid) scaffold in a rabbit model.

Focal full-thickness articular cartilage defects are challenging to repair. The purpose of this study was to find a simple, effective 1-step articular cartilage repair method. Because stem cell niches produce a microenvironment for stem cell self-renewal, proliferation, and differentiation, we integrated in situ bone marrow stem cells with an implanted poly(L-lactic-co-glycolic acid) (PLLGA) scaffold.

Differences in cerebral response to esophageal acid stimuli and psychological anticipation in GERD subtypes--an fMRI study.

Background: To evaluate whether there are differences in the cerebral response to intraesophageal acid and psychological anticipation stimuli among subtypes of gastroesophageal reflux disease (GERD).

Methods: Thirty nine patients with GERD and 11 healthy controls were enrolled in this study after gastroscopy and 24 hr pH monitoring. GERD subjects were divided into four subgroups: RE (reflux esophagitis), NERD+ (non-erosive reflux disease with excessive acid reflux), NERD-SI+ (normal acid exposure and positive symptom index) and NERD-SI+ (normal acid exposure and negative symptom index, but responded to proton pump inhibitor trial).